High-Performance Knowledge Extraction from Data on PC-Based Networks of Workstations

The automatic construction of classi ers programs able to correctly classify data collected from the real world is one of the major problems in pattern recognition and in a wide area related to Arti cial Intelligence, including Data Mining. In this paper we present G-Net, a distributed algorithm able to infer classi ers from pre-collected data, and its implementation on PC-based Networks of Workstations PC-NOWs. In order to e ectively exploit the computing power provided by PCNOWs, G-Net incorporates a set of dynamic load distribution techniques that allow it to adapt its behavior to variations in the computing power due to resource contention. Moreover, it is provided with a fault tolerance scheme that enables it to continue its computation even if the majority of the machines become unavailable during its execution.

• Type: Conference
• Year: 1999
• Where: IPPS
• Authors: Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bello
• Keywords: Arti Cial Intelligence, Classi Ers, Classi Ers Programs, Distributed And Parallel Computing, IPPS 1999